Adaptive Model-Based Control for Gas Turbine Actuation Faults
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Solution Overview
Problem
Current gas turbine engine control systems rely on sensor-based methods that do not effectively classify degradation of actuation subsystems, leading to suboptimal performance and operability due to lack of adaptability in response to faults, failures, or damage.
Innovation Solution
An adaptive model-based control system that uses constrained model-based control (CMBC) to diagnose faults, update model data, and adapt control strategies by identifying new constraints, objectives, and horizons, allowing for optimized engine performance and operability even under deteriorated conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor-based control systems are used, then the system structure is simple, but the system cannot classify degradation of actuation subsystems leading to suboptimal performance
Solution Approach 1:
The control system dynamically adapts its parameters and structure based on real-time fault detection and classification. The CMBC system continuously updates control horizons, prediction horizons, and constraint parameters according to the classified degradation level, transforming a static sensor-based system into a dynamic fault-adaptive system that optimizes performance under varying fault conditions.
Solution Approach 2:
The system changes control parameters (prediction horizon, control horizon, constraints) based on the classified fault condition. When degradation is detected and classified, the system modifies these parameters to maintain optimal performance, allowing the same hardware to achieve adaptive performance optimization through parameter reconfiguration rather than physical modification.
2Adaptability or versatility
If adaptive model-based control with fault classification is implemented, then performance optimization under fault conditions is achieved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary fault classification and degradation assessment before implementing control adjustments. By pre-classifying the type and severity of degradation, the system can select appropriate pre-defined control strategies and parameter sets, reducing the real-time computational burden while maintaining high adaptability to various fault conditions.
Solution Approach 2:
The control algorithm is segmented into distinct modules: fault detection, degradation classification, and adaptive control adjustment. This modular segmentation allows each module to be optimized independently and facilitates systematic processing of complex fault information, making the overall adaptive system more manageable and computationally efficient.
3Productivity
If real-time fault detection and classification is performed, then continuous optimization is achieved, but the processing time and computational resources increase
Solution Approach 1:
The system performs fault detection and classification at periodic intervals rather than continuously processing every data point. This periodic action maintains real-time optimization capability while reducing overall computational load and processing time, allowing the system to balance performance optimization with acceptable processing time requirements.
Data Source
AI summary
A method for controlling a gas turbine engine having a constrained model based control (CMBC) system. The method including obtaining information about a current and previous states of the engine, updating model data information in the CMBC and a parameter estimation system based on the obtained information, and identifying trends in the data based on the information. The method also includes diagnosing the engine, based on the identified trends, determining at least one of a new constraint, objective, initial condition, model characteristic, prediction horizon, and control horizon for the control system based on the diagnosing step if the diagnosing step identified a fault condition, and adapting the CMBC system based on the at least one new constraint, objective, initial condition, model characteristic, prediction and control horizon. The method further includes generating at least on control command based on the adapting and commanding an actuator based on the control command.